| name | bayesian-synthesis |
| description | Use Bayesian reasoning to synthesize evidence and update beliefs. Leverages the Arbiter persona's methodology for calibrated decision-making. |
| version | 1.0.0 |
| author | Hermes Ouroboros |
| license | MIT |
| metadata | {"hermes":{"tags":["Council","Bayesian","Decision-Making","Synthesis","Reasoning"],"related_skills":["multi-perspective-analysis","adversarial-critique"]}} |
Bayesian Synthesis
The Arbiter persona uses explicit Bayesian reasoning to synthesize multiple perspectives into a calibrated verdict. Use this approach when you need to make decisions under uncertainty.
The Bayesian Method
- State your prior: What do you believe before seeing any arguments? (0-100%)
- Update on evidence: For each new piece of evidence, adjust your belief
- State your posterior: Final belief after all updates, with confidence interval
- Identify remaining uncertainty: What would change your mind?
When to Use
- Decision-making under uncertainty: Multiple valid options, unclear trade-offs
- Forecasting: Predicting outcomes with limited information
- Evidence synthesis: Combining multiple studies or data sources
- Belief calibration: Checking if your confidence matches available evidence
How to Use
Full Council (recommended for important decisions)
council_query(
question="What is the probability that Ethereum's market cap surpasses Bitcoin's by 2028?",
context="Consider technical fundamentals, adoption metrics, regulatory landscape, and historical precedent."
)
The Arbiter's synthesis will include:
- PRIOR: Starting belief before arguments
- EVIDENCE UPDATES: How each persona's argument shifts the belief
- POSTERIOR: Final calibrated estimate
- KEY DISAGREEMENTS: Unresolved tensions
- FINAL VERDICT: Actionable recommendation
Quick Evaluation
council_evaluate(
content="[your analysis here]",
question="Is this analysis well-calibrated?",
criteria=["calibration", "evidence_quality", "uncertainty_handling"]
)
Claim Review With Evidence
council_review_claim(
claim="This library supports JSON mode on every OpenAI-compatible provider",
context="We plan to rely on response_format for production parsing",
evidence_search=true
)
Use verified_sources for retrieved evidence. Treat URLs in sources as model-cited unless they also appear in verified_sources.
Interpreting Bayesian Updates
The Arbiter reports updates like:
PRIOR: 40%
Advocate's impact: +15% (strong technical argument, but speculative)
Skeptic's impact: -10% (valid concern about regulatory risk)
Oracle's impact: +5% (base rate of paradigm shifts is low but non-zero)
Contrarian's impact: -5% (alternative framing worth considering)
POSTERIOR: 45% (40 + 15 - 10 + 5 - 5)
Key insight: The magnitude of each update reflects evidence quality:
- Large updates (>10%): Strong empirical evidence or fatal logical flaw
- Medium updates (5-10%): Credible argument with partial evidence
- Small updates (<5%): Theoretical concern or weak analogy
Evidence Hierarchy
The Arbiter weights evidence by quality:
- Empirical data (strongest): Controlled studies, historical statistics
- Logical argument: Valid deductive reasoning from established premises
- Expert consensus: Agreement among domain experts
- Historical analogies: Similar past situations and their outcomes
- Intuition (weakest): Gut feeling or aesthetic preference
Common Pitfalls
- Anchoring: Don't let your prior dominate. If evidence is strong, update significantly.
- Base rate neglect: Oracle's data matters. Most ventures fail, most predictions are overconfident.
- Conjunction fallacy: A specific story isn't more likely just because it's detailed.
- Confirmation bias: Pay special attention to the Skeptic's counter-evidence.
